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Transcript of Exploiting indirect neighbors and topological weight to predict protein function from protein–...
Exploiting indirect neighbors and topological weight to predict protein function from protein–protein interactions
Hon Nian Chua, Wing-Kin Sung and Limsoon Wong
Motivation
Predicting the protein function from Protein-
protein interaction data.
• Previous studies considers level 1 neighbors• Can level-2 neighbors play an significant role
in this prediction?
Summarizing the output of the study
• level-2 neighbors does show functional association.• A significant no. Proteins were observed to be
having associations with level-2 neighbors but not with level-1 neighbors.
• A predicting algorithm:• 1) weight Level 1 & 2 neighbors based on
functional similarity.• 2) each function was also allotted a score based
on its weighted frequency in neighbors
Conventional approaches
• using only direct interactions i.e level-1 neighbors
• Consider a radius in the interaction neighborhood network
• Calculate a functional distance and use clustering to make some functional classes.
Protein-Protein interactions as an undirected graph
• G=(V,E)• (u, v) as two protein nodes • And edge e between them as interaction• U and v being , K-level neighbors– concept of
path with k-edges between u and v.
• Set of neighbors-- Sk
Indirect Functional Association
Significance
• out of 4162 annotated proteins, only 1999 or 48% share some function with level-1 neighbors.
Sets of neighborhood pairs
Simple neighbor counting
•
• Discuss– M and N• M- total predicted N-total functions known
The Algorithm
• 1) Functional similarity Weight
Previous approaches use CD-distance between proteins u and v given by
A simple example
• When a fraction ‘x’ of protein’s ‘u’s neighbors is common to protein ‘v’s neighbors then x is proportional to the probability that u’s functions are shared with v through common neighbors. (and vice versa for y protion of v ‘s neighbor common with neighbor of u)
•
• 2) integrating reliability of experimental sources:The prediction results can be improved by taking
differences in reliability of sources into account. So between u and v , the reliability of the interaction is estimated as:
• i source no. Euv set of sources with interaction u, v n no . Of times in which interaction btween u and v was observed
So, integrated equation becomes
Transitive functional Association
• If u is similar to w and w is similar to v then there can be a similarity between u and v given by:
•
Functional Similarity Weighted Averaging
• the likelihood of protein p having function x:
• STR(u,v) Transitive FS weight• r_int fraction of all the proteins who share this considered function• Sigma(p,x) = 1 if p has function x else =0
• Pi_x frequency of function x in proteins
Results
• 1) ORIGINAL NEIGHBOR COUNTING• 2) Neighbor counting with FS-weight• 3) scheme in (2)+ level-2 neighbors are
considered.
Comparison with other schemes
Improvements?
• Threshold at level-2..